dc.contributor.author | Ul Amin, Sareer | |
dc.contributor.author | Ullah, Mohib | |
dc.contributor.author | Sajjad, Muhammad | |
dc.contributor.author | Alaya Cheikh, Faouzi | |
dc.contributor.author | Hijji, Mohammad | |
dc.contributor.author | Hijji, Abdulrahman | |
dc.contributor.author | Khan, Muhammad (SKKU) | |
dc.date.accessioned | 2023-02-27T12:13:38Z | |
dc.date.available | 2023-02-27T12:13:38Z | |
dc.date.created | 2023-02-15T12:38:14Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 2227-7390 | |
dc.identifier.uri | https://hdl.handle.net/11250/3054221 | |
dc.description.abstract | Surveillance systems regularly create massive video data in the modern technological era, making their analysis challenging for security specialists. Finding anomalous activities manually in these enormous video recordings is a tedious task, as they infrequently occur in the real world. We proposed a minimal complex deep learning-based model named EADN for anomaly detection that can operate in a surveillance system. At the model’s input, the video is segmented into salient shots using a shot boundary detection algorithm. Next, the selected sequence of frames is given to a Convolutional Neural Network (CNN) that consists of time-distributed 2D layers for extracting salient spatiotemporal features. The extracted features are enriched with valuable information that is very helpful in capturing abnormal events. Lastly, Long Short-Term Memory (LSTM) cells are employed to learn spatiotemporal features from a sequence of frames per sample of each abnormal event for anomaly detection. Comprehensive experiments are performed on benchmark datasets. Additionally, the quantitative results are compared with state-of-the-art methods, and a substantial improvement is achieved, showing our model’s effectiveness. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | EADN: An Efficient Deep Learning Model for Anomaly Detection in Videos | en_US |
dc.title.alternative | EADN: An Efficient Deep Learning Model for Anomaly Detection in Videos | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.volume | 10 | en_US |
dc.source.journal | Mathematics | en_US |
dc.source.issue | 9 | en_US |
dc.identifier.doi | https://doi.org/10.3390/math10091555 | |
dc.identifier.cristin | 2126285 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |